AT-VLA: 適応的触覚注入による視覚言語行動モデルのフィードバック反応向上
AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models
接触を伴う操作タスクで視覚言語行動モデルの性能を高めるため、適応的に触覚情報を注入する仕組みと、高速な触覚反応を可能にする二重ストリーム構造を提案した。
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著者: Xiaoqi Li, Muhe Cai, Jiadong Xu, Juan Zhu, Hongwei Fan, Yan Shen, Guangrui Ren, Hao Dong
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face challenges in contact-rich manipulation scenarios that require precise physical interactions. To address this limitation, recent studies have attempted to incorporate tactile signals during downstream tasks, enabling pretrained VLAs to interpret tactile feedback. Nevertheless, introducing new modalities during finetuning, which are rarely present in the pretrain stage, may disrupt the pretrained capabilities of VLAs. In addition, the inherently slow inference speed of VLAs hampers real-time responsiveness and limits the effective utilization of tactile feedback for action adjustment. To overcome these challenges, we propose Adaptive Tactile Vision-Language-Action (AT-VLA), which introduces a novel Adaptive Tactile Injection mechanism. This mechanism dynamically determines the appropriate timing and locations for tactile injection, incorporating only when it significantly contributes to action generation, thereby minimizing interference with pretrained representations. Furthermore, to enable rapid and accurate tactile responses, we propose a Tactile Reaction Dual-Stream mechanism, which decouples sensory processing into a slow visual-language stream for low-frequency perceptual reasoning and a fast tactile control stream for high-frequency physical interaction understanding, achieving real-time close-loop responses within 0.04 s. Real-world experiments thoroughly validate the effectiveness of AT-VLA in contact-rich manipulation tasks. The project page is available at: https://sites.google.com/view/at-vla.
関連論文
- ViTaR: 基盤VLA操作のための視触覚残差適応VLA/触覚/操作
- TACO: 触覚を考慮した世界モデルによるスケーラブルなVLAポストトレーニングの自己修正VLA/触覚/操作
- τ: 未来の視覚的監督から学習する触覚拡張型視覚言語行動モデルVLA/触覚/操作
- N0-VTLA：潜在触覚トークンによる視覚・触覚・言語・行動モデルのスケーリングVLA/触覚/操作
- TacVLA: 接触認識型触覚融合による堅牢な視覚言語行動操作VLA/触覚/操作
